MiniCPM V 4 OpenVINO INT4
This repository contains the OpenVINO IR format of the openbmb/MiniCPM-V-4 model, heavily optimized for CPU inference.
Source du modèle
- Type
- Other
- Taille du fichier
- 1,04 MB–1,74 GB
- Modèle de base
- openbmb/MiniCPM-V-4
- Version
- vRevision a4fe669
- Mis à jour
- 12 oct. 2026
- Téléchargements
- 8
- Source
- Hugging Face
Description de la source
MiniCPM-V-4 OpenVINO INT4 (Preserved FP16 Vision)
This repository contains the OpenVINO IR format of the openbmb/MiniCPM-V-4 model, heavily optimized for CPU inference.
Optimization Details
To ensure optimal performance without sacrificing multimodal capabilities, a selective quantization strategy was applied:
- Language Model (LLM): Quantized to INT4 (Asymmetric, Group Size 128) for maximum memory efficiency and CPU inference speed.
- Vision Encoder & Resampler: Preserved in FP16. This surgical approach ensures that the model's visual acuity (including text recognition, UI elements, and fine details) remains identical to the original unquantized model.
Hardware Targeting
This format is designed to run efficiently on standard CPUs, fully utilizing AVX-512 and VNNI instruction sets (e.g., Intel Ice Lake and newer) without requiring dedicated GPU hardware.
Sources
1 source
Sources
1 sourceVérifié 27 août
Download files
Artefacts du modèle
6 artefacts
Artefacts du modèle
6 artefactsopenvino_text_embeddings_model.bin
bin · 359 MB · SHA-256 2a039d1d977b…7881 · Hugging Face
Download fileopenvino_vision_embeddings_model.bin
bin · 797 MB · SHA-256 0cabc71c2bb7…74ce · Hugging Face
Download fileExtraits de sources
2 extraits
Extraits de sources
2 extraits--- base_model: openbmb/MiniCPM-V-4 base_model_relation: quantized quantized_by: HyX3 license: apache-2.0 tags: - quantization - openvino - int4 - vision - multimodal - minicpm pipeline_tag: image-to-text --- # MiniCPM-V-4 OpenVINO INT4 (Preserved FP16 Vision) This repository contains the OpenVINO IR format of the `openbmb/MiniCPM-V-4` model, heavily optimized for CPU inference. ## Optimization Details To ensure optimal performance without sacrificing multimodal capabilities, a selective quantization strategy was applied: - **Language Model (LLM):** Quantized to **INT4** (Asymmetric, Group Size 128) for maximum memory efficiency and CPU inference speed. - **Vision Encoder & Resampler:** Preserved in **FP16**. This surgical approach ensures that the model's visual acuity (including text recognition, UI elements, and fine details) remains identical to the original unquantized model. ## Hardware Targeting This format is designed to run efficiently on standard CPUs, fully utilizing AVX-512 and VNNI instruction sets (e.g., Intel Ice Lake and newer) without requiring dedicated GPU hardware.
Source context: 8 downloads · 0 likes · Pipeline image-to-text · Repo HyX3/MiniCPM-V-4-OpenVINO-INT4
